rrij

Since causal paths from data are important for all sciences, the
package provides many sophisticated functions. causeSummBlk()
gives easy-to-interpret causal paths.  Let Z denote control variables and compare
two flipped kernel regressions: X=f(Y, Z)+e1 and Y=g(X,Z)+e2. Our criterion Cr1
says that if |e1*Y|>|e2*X| then variation in X is more "exogenous or independent"
than in Y and causal path is X to Y. Criterion Cr2 requires |e2|<|e1|. These
inequalities between many absolute values are quantified by four orders of
stochastic dominance. Our third criterion Cr3 for the causal path X to Y
requires new generalized partial correlations to satisfy |r*(x|y,z)|< |r*(y|x,z)|.
The function parcorVec() reports generalized partials between the first
variable and all others.  The package provides several R functions including
get0outliers() for outlier detection, bigfp() for numerical integration by the
trapezoidal rule, stochdom2() for stochastic dominance, pillar3D() for 3D charts,
canonRho() for generalized canonical correlations, depMeas() measures nonlinear
dependence, and causeSummary(mtx) reports summary of causal paths among matrix columns
is easiest to use. Portfolio selection: decileVote(), momentVote(), dif4mtx(),
exactSdMtx() can rank several stocks. Several functions whose names begin with 'boot' provide bootstrap
statistical inference including a new bootGcRsq() test for "Granger-causality"
allowing nonlinear relations. See five vignettes of the package for theory
and usage tips. See Vinod (2019) \doi{10.1080/03610918.2015.1122048}.

Hrishikesh Vinod

rrij: internal rrij

Description

Usage

Arguments